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Quantum Challenges: Hardware and Software Perspectives

Aida Todri‐Sanial

Published Jan 1, 2020• 0 citations
OpenAlex

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Quantum computing is transitioning from laboratory research to industrial deployment, yet significant challenges persist: system scalability and performance, fabrication yields, and the advancement of algorithms and applications. We emphasize that in building quantum computers -- spanning quantum chips, system integration, instruction sets, algorithms, and middleware such as quantum error correction schemes -- design is everywhere. In this paper, we advocate for a holistic design perspective in quantum computing, a perspective we argue is pivotal to unlocking innovative co-design opportunities and addressing the aforementioned key challenges. To equip readers with sufficient background for exploring co-optimization opportunities, we detail how interconnected computational methods and tools collaborate to enable end-to-end quantum computer design. This coverage encompasses critical stages -- such as chip layout design automation, high-fidelity system-level simulation, Hamiltonian derivation for quantum system modeling, control pulse simulation, decoherence analysis, and physical verification and testing -- followed by quantum instruction set design. We then proceed to quantum system and software development, including quantum circuit synthesis, quantum error correction and fault tolerance, and logic verification and testing. Through these discussions, we illustrate with concrete examples -- including co-optimizing quantum instruction sets with algorithmic considerations, customizing error correction circuits to hardware-specific constraints, and streamlining quantum chip design through tailored code design, among others. We hope that the detailed end-to-end design workflow as well as these examples will foster dialogue between the hardware and software communities, ultimately facilitating the translation of meaningful research findings into future quantum hardware implementations.

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The article provides an overview of the current state and future challenges of quantum software engineering as quantum computing transitions from proof-of-concept to potential industrial adoption. While significant progress has been made in quantum hardware and cloud-based execution platforms, the article argues that innovation will depend critically on advances in software tools, programming abstractions, and engineering methodologies. Focusing on the constraints of the Noisy Intermediate-Scale Quantum (NISQ) era, it examines challenges related to hardware heterogeneity, software portability, hybrid classical–quantum architectures, and the lack of mature development environments. The article also highlights the urgent need for a new generation of quantum software engineers and coherent educational programmes, particularly in Europe, to bridge skills gaps and support the long-term evolution toward fault-tolerant quantum computing. This article is published in IE Perspectives, Issue 1 (January 2026). Full issue available at: https://doi.org/10.5281/zenodo.18376222
Quantum computing is a disruptive technology with the potential to transform various fields. It has predicted abilities to solve complex computational problems beyond the reach of classical computers. However, developing quantum software faces significant challenges. Quantum hardware is yet limited in size and unstable with errors and noise. A shortage of skilled developers and a lack of standardization delay adoption. Quantum hardware is in the process of maturing and is constantly changing its characteristics rendering algorithm design increasingly complex, requiring innovative solutions. Project "Towards reliable quantum software development: Approaches and use-cases" TORQS has studied the dilemma of reliable software development and potential for quantum computing for Finnish industries from multidisciplinary points of views. Here we condense the main observations and results of the project into an essay roadmap and timeline for investing in quantum software, algorithms, hardware, and business.
Quantum computing (QC) has experienced rapid growth in recent years with the advent of robust programming environments, readily accessible software simulators and cloud-based QC hardware platforms, and growing interest in learning how to design useful methods that leverage this emerging technology for practical applications. From the perspective of the field of visualization, this article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.
Quantum computing is transitioning from laboratory research to industrial deployment, yet significant challenges persist: system scalability and performance, fabrication yields, and the advancement of algorithms and applications. We emphasize that in building quantum computers -- spanning quantum chips, system integration, instruction sets, algorithms, and middleware such as quantum error correction schemes -- design is everywhere. In this paper, we advocate for a holistic design perspective in quantum computing, a perspective we argue is pivotal to unlocking innovative co-design opportunities and addressing the aforementioned key challenges. To equip readers with sufficient background for exploring co-optimization opportunities, we detail how interconnected computational methods and tools collaborate to enable end-to-end quantum computer design. This coverage encompasses critical stages -- such as chip layout design automation, high-fidelity system-level simulation, Hamiltonian derivation for quantum system modeling, control pulse simulation, decoherence analysis, and physical verification and testing -- followed by quantum instruction set design. We then proceed to quantum system and software development, including quantum circuit synthesis, quantum error correction and fault tolerance, and logic verification and testing. Through these discussions, we illustrate with concrete examples -- including co-optimizing quantum instruction sets with algorithmic considerations, customizing error correction circuits to hardware-specific constraints, and streamlining quantum chip design through tailored code design, among others. We hope that the detailed end-to-end design workflow as well as these examples will foster dialogue between the hardware and software communities, ultimately facilitating the translation of meaningful research findings into future quantum hardware implementations.